===============
Rope Overview
===============
The purpose of this file is to give an overview of some of rope's
features. It is incomplete. And some of the features shown here are
old and do not show what rope can do in extremes. So if you really
want to feel the power of rope try its features and see its unit
tests.
This file is more suitable for the users. Developers who plan to use
rope as a library might find library.txt_ more useful.
.. contents:: Table of Contents
.. _library.txt: library.html
``.ropeproject`` Folder
=======================
Rope uses a folder inside projects for holding project configuration
and data. Its default name is ``.ropeproject``, but it can be
changed (you can even tell rope not to create this folder).
Currently it is used for things such as:
* There is a ``config.py`` file in this folder in which you can change
project configurations. Have look at the default ``config.py`` file
(is created when it does not exist) for more information.
* It can be used for saving project history, so that the next time you
open the project you can undo past changes.
* It can be used for saving object information to help rope object
inference.
* It can be used for saving global names cache which is used in
auto-import.
You can change what to save and what not to in the ``config.py`` file.
Refactorings
============
This section shows some random refactorings that you can perform using
rope.
Renaming Attributes
-------------------
Consider we have::
class AClass(object):
def __init__(self):
self.an_attr = 1
def a_method(self, arg):
print self.an_attr, arg
a_var = AClass()
a_var.a_method(a_var.an_attr)
After renaming ``an_attr`` to ``new_attr`` and ``a_method`` to
``new_method`` we'll have::
class AClass(object):
def __init__(self):
self.new_attr = 1
def new_method(self, arg):
print self.new_attr, arg
a_var = AClass()
a_var.new_method(a_var.new_attr)
Renaming Function Keyword Parameters
------------------------------------
On::
def a_func(a_param):
print a_param
a_func(a_param=10)
a_func(10)
performing rename refactoring on any occurrence of ``a_param`` will
result in::
def a_func(new_param):
print new_param
a_func(new_param=10)
a_func(10)
Renaming modules
----------------
Consider the project tree is something like::
root/
mod1.py
mod2.py
``mod1.py`` contains::
import mod2
from mod2 import AClass
mod2.a_func()
a_var = AClass()
After performing rename refactoring one of the ``mod2`` occurrences in
`mod1` we'll get::
import newmod
from newmod import AClass
newmod.a_func()
a_var = AClass()
and the new project tree would be::
root/
mod1.py
newmod.py
Renaming Occurrences In Strings And Comments
--------------------------------------------
You can tell rope to rename all occurrences of a name in comments and
strings. This can be done by passing ``docs=True`` to
`Rename.get_changes()` method. Rope renames names in comments and
strings only where the name is visible. For example in::
def f():
a_var = 1
# INFO: I'm printing `a_var`
print 'a_var = %s' % a_var
# f prints a_var
after we rename the `a_var` local variable in `f()` to `new_var` we
would get::
def f():
new_var = 1
# INFO: I'm printing `new_var`
print 'new_var = %s' % new_var
# f prints a_var
This makes it safe to assume that this option does not perform wrong
renames most of the time.
This also changes occurrences inside evaluated strings::
def func():
print 'func() called'
eval('func()')
After renaming `func` to `newfunc` we should have::
def newfunc():
print 'newfunc() called'
eval('newfunc()')
Rename When Unsure
------------------
This option tells rope to rename when it doesn't know whether it is an
exact match or not. For example after renaming `C.a_func` when the
'rename when unsure' option is set in::
class C(object):
def a_func(self):
pass
def a_func(arg):
arg.a_func()
C().a_func()
we would have::
class C(object):
def new_func(self):
pass
def a_func(arg):
arg.new_func()
C().new_func()
Note that the global `a_func` was not renamed because we are sure that
it is not a match. But when using this option there might be some
unexpected renames. So only use this option when the name is almost
unique and is not defined in other places.
Move Method Refactoring
-----------------------
It happens when you perform move refactoring on a method of a class.
In this refactoring, a method of a class is moved to the class of one
of its attributes. The old method will call the new method. If you
want to change all of the occurrences of the old method to use the new
method you can inline it afterwards.
For instance if you perform move method on `a_method` in::
class A(object):
pass
class B(object):
def __init__(self):
self.attr = A()
def a_method(self):
pass
b = B()
b.a_method()
You will be asked for the destination field and the name of the new
method. If you use ``attr`` and ``new_method`` in these fields
and press enter, you'll have::
class A(object):
def new_method(self):
pass
class B(object):
def __init__(self):
self.attr = A()
def a_method(self):
return self.attr.new_method()
b = B()
b.a_method()
Now if you want to change the occurrences of `B.a_method()` to use
`A.new_method()`, you can inline `B.a_method()`::
class A(object):
def new_method(self):
pass
class B(object):
def __init__(self):
self.attr = A()
b = B()
b.attr.new_method()
Moving Fields
-------------
Rope does not have a separate refactoring for moving fields. Rope's
refactorings are very flexible, though. You can use the rename
refactoring to move fields. For instance::
class A(object):
pass
class B(object):
def __init__(self):
self.a = A()
self.attr = 1
b = B()
print(b.attr)
consider we want to move `attr` to `A`. We can do that by renaming `attr`
to `a.attr`::
class A(object):
pass
class B(object):
def __init__(self):
self.a = A()
self.a.attr = 1
b = B()
print(b.a.attr)
You can move the definition of `attr` manually.
Extract Method
--------------
In these examples ``${region_start}`` and ``${region_end}`` show the
selected region for extraction::
def a_func():
a = 1
b = 2 * a
c = ${region_start}a * 2 + b * 3${region_end}
After performing extract method we'll have::
def a_func():
a = 1
b = 2 * a
c = new_func(a, b)
def new_func(a, b):
return a * 2 + b * 3
For multi-line extractions if we have::
def a_func():
a = 1
${region_start}b = 2 * a
c = a * 2 + b * 3${region_end}
print b, c
After performing extract method we'll have::
def a_func():
a = 1
b, c = new_func(a)
print b, c
def new_func(a):
b = 2 * a
c = a * 2 + b * 3
return b, c
Extracting Similar Expressions/Statements
-----------------------------------------
When performing extract method or local variable refactorings you can
tell rope to extract similar expressions/statements. For instance
in::
if True:
x = 2 * 3
else:
x = 2 * 3 + 1
Extracting ``2 * 3`` will result in::
six = 2 * 3
if True:
x = six
else:
x = six + 1
Extract Method In staticmethods/classmethods
--------------------------------------------
The extract method refactoring has been enhanced to handle static and
class methods better. For instance in::
class A(object):
@staticmethod
def f(a):
b = a * 2
if you extract ``a * 2`` as a method you'll get::
class A(object):
@staticmethod
def f(a):
b = A.twice(a)
@staticmethod
def twice(a):
return a * 2
Inline Method Refactoring
-------------------------
Inline method refactoring can add imports when necessary. For
instance consider ``mod1.py`` is::
import sys
class C(object):
pass
def do_something():
print sys.version
return C()
and ``mod2.py`` is::
import mod1
c = mod1.do_something()
After inlining `do_something`, ``mod2.py`` would be::
import mod1
import sys
print sys.version
c = mod1.C()
Also rope can inline class methods; for instance in::
class C(object):
@classmethod
def say_hello(cls, name):
return 'Saying hello to %s from %s' % (name, cls.__name__)
hello = C.say_hello('Rope')
inlining `say_hello` will result in::
class C(object):
pass
hello = 'Saying hello to %s from %s' % ('Rope', C.__name__)
Inlining Parameters
-------------------
`rope.refactor.inline.create_inline()` creates an `InlineParameter`
object when it is performed on a parameter. It passes the default
value of the parameter wherever its function is called without passing
it. For instance in::
def f(p1=1, p2=1):
pass
f(3)
f()
f(3, 4)
after inlining p2 parameter will have::
def f(p1=1, p2=1):
pass
f(3, 2)
f(p2=2)
f(3, 4)
Use Function Refactoring
------------------------
It tries to find the places in which a function can be used and
changes the code to call it instead. For instance if mod1 is::
def square(p):
return p ** 2
my_var = 3 ** 2
and mod2 is::
another_var = 4 ** 2
if we perform "use function" on square function, mod1 will be::
def square(p):
return p ** 2
my_var = square(3)
and mod2 will be::
import mod1
another_var = mod1.square(4)
Automatic Default Insertion In Change Signature
-----------------------------------------------
The `rope.refactor.change_signature.ArgumentReorderer` signature
changer takes a parameter called ``autodef``. If not `None`, its
value is used whenever rope needs to insert a default for a parameter
(that happens when an argument without default is moved after another
that has a default value). For instance in::
def f(p1, p2=2):
pass
if we reorder using::
changers = [ArgumentReorderer([1, 0], autodef='1')]
will result in::
def f(p2=2, p1=1):
pass
Sorting Imports
---------------
Organize imports sorts imports, too. It does that according to
:PEP:`8`::
[__future__ imports]
[standard imports]
[third-party imports]
[project imports]
[the rest of module]
Handling Long Imports
---------------------
``Handle long imports`` command trys to make long imports look better by
transforming ``import pkg1.pkg2.pkg3.pkg4.mod1`` to ``from
pkg1.pkg2.pkg3.pkg4 import mod1``. Long imports can be identified
either by having lots of dots or being very long. The default
configuration considers imported modules with more than 2 dots or with
more than 27 characters to be long.
Stoppable Refactorings
----------------------
Some refactorings might take a long time to finish (based on the size
of your project). The `get_changes()` method of these refactorings
take a parameter called `task_handle`. If you want to monitor or stop
these refactoring you can pass a `rope.refactor.
taskhandle.TaskHandle` to this method. See `rope.refactor.taskhandle`
module for more information.
Basic Implicit Interfaces
-------------------------
Implicit interfaces are the interfaces that you don't explicitly
define; But you expect a group of classes to have some common
attributes. These kinds of interfaces are very common in dynamic
languages; Since we only have implementation inheritance and not
interface inheritance. For instance::
class A(object):
def count(self):
pass
class B(object):
def count(self):
pass
def count_for(arg):
return arg.count()
count_for(A())
count_for(B())
Here we know that there is an implicit interface defined by the
function `count_for` that provides `count()`. Here when we rename
`A.count()` we expect `B.count()` to be renamed, too. Currently rope
supports a basic form of implicit interfaces. When you try to rename
an attribute of a parameter, rope renames that attribute for all
objects that have been passed to that function in different call
sites. That is renaming the occurrence of `count` in `count_for`
function to `newcount` will result in::
class A(object):
def newcount(self):
pass
class B(object):
def newcount(self):
pass
def count_for(arg):
return arg.newcount()
count_for(A())
count_for(B())
This also works for change method signature. Note that this feature
relies on rope's object inference mechanisms to find out the
parameters that are passed to a function.
Restructurings
--------------
`rope.refactor.restructure` can be used for performing restructurings.
A restructuring is a program transformation; not as well defined as
other refactorings like rename. Let's see some examples (for more
examples, see the pydocs in `rope.base.restructure` module).
Example 1
'''''''''
In its basic form we have a pattern and a goal. Consider we were not
aware of the ``**`` operator and wrote our own ::
def pow(x, y):
result = 1
for i in range(y):
result *= x
return result
print pow(2, 3)
Now that we know ``**`` exists we want to use it wherever `pow` is
used (there might be hundreds of them!). We can use a pattern like::
pattern: pow(${param1}, ${param2})
Goal can be something like::
goal: ${param1} ** ${param2}
Note that ``${...}`` can be used to match expressions. By default
every expression at that point will match.
You can use the matched names in goal and they will be replaced with
the string that was matched in each occurrence. So the outcome of our
restructuring will be::
def pow(x, y):
result = 1
for i in range(y):
result *= x
return result
print 2 ** 3
It seems to be working but what if `pow` is imported in some module or
we have some other function defined in some other module that uses the
same name and we don't want to change it. Wildcard arguments come to
rescue. Wildcard arguments is a mapping; Its keys are wildcard names
that appear in the pattern (the names inside ``${...}``).
The values are the parameters that are passed to wildcard matchers.
The arguments a wildcard takes is based on its type.
For checking the type of a wildcard, we can pass ``type=value`` as an
arg; ``value`` should be resolved to a python variable (or reference).
For instance for showing `pow` in this example we can use `mod.pow`.
As you see this string should start from module name. For referencing
python builtin types and functions you can use `__builtin__` module
(for instance `__builtin__.int`).
For solving the mentioned problem we change our `pattern`. But `goal`
remains the same::
pattern: ${pow_func}(${param1}, ${param2})
goal: ${param1} ** ${param2}
Consider the name of the module containing our `pow` function is
`mod`. ``args`` can be::
pow_func: name=mod.pow
If we need to pass more arguments to a wildcard matcher we can use
``,`` to separate them. Such as ``name: type=mod.MyClass,exact``.
This restructuring handles aliases; like in::
mypow = pow
result = mypow(2, 3)
Transforms into::
mypow = pow
result = 2 ** 3
If we want to ignore aliases we can pass ``exact`` as another wildcard
argument::
pattern: ${pow}(${param1}, ${param2})
goal: ${param1} ** ${param2}
args: pow: name=mod.pow, exact
``${name}``, by default, matches every expression at that point; if
``exact`` argument is passed to a wildcard only the specified name
will match (for instance, if ``exact`` is specified , ``${name}``
matches ``name`` and ``x.name`` but not ``var`` nor ``(1 + 2)`` while
a normal ``${name}`` can match all of them).
For performing this refactoring using rope library see `library.txt`_.
Example 2
'''''''''
As another example consider::
class A(object):
def f(self, p1, p2):
print p1
print p2
a = A()
a.f(1, 2)
Later we decide that `A.f()` is doing too much and we want to divide
it to `A.f1()` and `A.f2()`::
class A(object):
def f(self, p1, p2):
print p1
print p2
def f1(self, p):
print p
def f2(self, p):
print p2
a = A()
a.f(1, 2)
But who's going to fix all those nasty occurrences (actually this
situation can be handled using inline method refactoring but this is
just an example; consider inline refactoring is not implemented yet!).
Restructurings come to rescue::
pattern: ${inst}.f(${p1}, ${p2})
goal:
${inst}.f1(${p1})
${inst}.f2(${p2})
args:
inst: type=mod.A
After performing we will have::
class A(object):
def f(self, p1, p2):
print p1
print p2
def f1(self, p):
print p
def f2(self, p):
print p2
a = A()
a.f1(1)
a.f2(2)
Example 3
'''''''''
If you like to replace every occurrences of ``x.set(y)`` with ``x =
y`` when x is an instance of `mod.A` in::
from mod import A
a = A()
b = A()
a.set(b)
We can perform a restructuring with these information::
pattern: ${x}.set(${y})
goal: ${x} = ${y}
args: x: type=mod.A
After performing the above restructuring we'll have::
from mod import A
a = A()
b = A()
a = b
Note that ``mod.py`` contains something like::
class A(object):
def set(self, arg):
pass
Issues
''''''
Pattern names can appear only at the start of an expression. For
instance ``var.${name}`` is invalid. These situations can usually be
fixed by specifying good checks, for example on the type of `var` and
using a ``${var}.name``.
Object Inference
================
This section is a bit out of date. Static object inference can do
more than described here (see unittests). Hope to update this
someday!
Static Object Inference
-----------------------
::
class AClass(object):
def __init__(self):
self.an_attr = 1
def call_a_func(self):
return a_func()
def a_func():
return AClass()
a_var = a_func()
#a_var.${codeassist}
another_var = a_var
#another_var.${codeassist}
#another_var.call_a_func().${codeassist}
Basic support for builtin types::
a_list = [AClass(), AClass()]
for x in a_list:
pass
#x.${codeassist}
#a_list.pop().${codeassist}
a_dict = ['text': AClass()]
for key, value in a_dict.items():
pass
#key.${codeassist}
#value.${codeassist}
Enhanced static returned object inference::
class C(object):
def c_func(self):
return ['']
def a_func(arg):
return arg.c_func()
a_var = a_func(C())
Here rope knows that the type of a_var is a `list` that holds `str`\s.
Supporting generator functions::
class C(object):
pass
def a_generator():
yield C()
for c in a_generator():
a_var = c
Here the objects `a_var` and `c` hold are known.
Rope collects different types of data during SOA, like per name data
for builtin container types::
l1 = [C()]
var1 = l1.pop()
l2 = []
l2.append(C())
var2 = l2.pop()
Here rope can easily infer the type of `var1`. But for knowing the
type of `var2`, it needs to analyze the items inserted into `l2` which
might happen in other modules. Rope can do that by running SOA on
that module.
You might be wondering is there any reason for using DOA instead of
SOA. The answer is that DOA might be more accurate and handles
complex and dynamic situations. For example in::
def f(arg):
return eval(arg)
a_var = f('C')
SOA can no way conclude the object `a_var` holds but it is really
trivial for DOA. What's more SOA only analyzes calls in one module
while DOA analyzes any call that happens when running a module. That
is, for achieving the same result as DOA you might need to run SOA on
more than one module and more than once (not considering dynamic
situations.) One advantage of SOA is that it is much faster than DOA.
Dynamic Object Analysis
-----------------------
`PyCore.run_module()` runs a module and collects object information if
``perform_doa`` project config is set. Since as the program runs rope
gathers type information, the program runs much slower. After the
program is run, you can get better code assists and some of the
refactorings perform much better.
``mod1.py``::
def f1(param):
pass
#param.${codeassist}
#f2(param).${codeassist}
def f2(param):
#param.${codeassist}
return param
Using code assist in specified places does not give any information
and there is actually no information about the return type of `f2` or
`param` parameter of `f1`.
``mod2.py``::
import mod1
class A(object):
def a_method(self):
pass
a_var = A()
mod1.f1(a_var)
Retry those code assists after performing DOA on `mod2` module.
Builtin Container Types
'''''''''''''''''''''''
Builtin types can be handled in a limited way, too::
class A(object):
def a_method(self):
pass
def f1():
result = []
result.append(A())
return result
returned = f()
#returned[0].${codeassist}
Test the the proposed completions after running this module.
Guessing Function Returned Value Based On Parameters
----------------------------------------------------
``mod1.py``::
class C1(object):
def c1_func(self):
pass
class C2(object):
def c2_func(self):
pass
def func(arg):
if isinstance(arg, C1):
return C2()
else:
return C1()
func(C1())
func(C2())
After running `mod1` either SOA or DOA on this module you can test:
``mod2.py``::
import mod1
arg = mod1.C1()
a_var = mod1.func(arg)
a_var.${codeassist}
mod1.func(mod1.C2()).${codeassist}
Automatic SOA
-------------
When turned on, it analyzes the changed scopes of a file when saving
for obtaining object information; So this might make saving files a
bit more time consuming. By default, this feature is turned on, but
you can turn it off by editing your project ``config.py`` file, though
that is not recommended.
Validating Object DB
--------------------
Since files on disk change over time project objectdb might hold
invalid information. Currently there is a basic incremental objectdb
validation that can be used to remove or fix out of date information.
Rope uses this feature by default but you can disable it by editing
``config.py``.
Custom Source Folders
=====================
By default rope searches the project for finding source folders
(folders that should be searched for finding modules). You can add
paths to that list using ``source_folders`` project config. Note that
rope guesses project source folders correctly most of the time. You
can also extend python path using ``python_path`` config.
Version Control Systems Support
===============================
When you perform refactorings some files might need to be moved (when
renaming a module) or new files might be created. When you use a VCS
rope uses that to perform file system actions.
Currently rope supports Subversion (Uses `pysvn`_ library) and
Mercurial_. Rope uses Subversion if the `pysvn` module is available
and there is a `.svn` in project root. The Mercurial will be used if
`mercurial` module is available and there is a `.hg` in project root.
Rope assumes either all files are under version control in a project
or there is no version control at all. Also don't forget to commit
your changes yourself, rope doesn't do that.
Adding support for other VCSs is easy; have a look at
`library.txt`_.
.. _pysvn: http://pysvn.tigris.org
.. _Mercurial: http://selenic.com/mercurial